Apple Leaf Disease Detection: Machine Learning & Deep Learning Techniques

Early leaf disease detection is essential to mitigate potential crop loss and reduce reliance on chemical treatments. Here we have tried out several Machine Learning and Deep Learning Techniques for automatically detecting apple leaf diseases. Machine Learning techniques like Support Vector Machine, Random Forest, Naive Bayes, K–Nearest Neighbor, Decision Tree, and some Deep Learning methods like Sequential, and VGG-16. We have compared all these models and found that VGG-16 has more accurate results than the other models we used. The recommended model proposes profound figuring out how to improve the arrangement of apple leaf illness. Additionally, it demonstrates the highest validation accuracy of 97.23 percent on the dataset for apple leaf disease. Contrasted with some present futuristic, this approach performs better. Therefore, farmers can use the suggested system to streamline the process of classifying apple leaf disease and aid in the disease's early diagnosis and treatment.